The goal of this research is to unambiguously identify and quantitate at-risk yet salvageable brain tissue during acute human ischemic stroke using novel diffusion/perfusion MRI approaches. The ability to quantitatively differentiate ischemic from infarcted tissue has long been sought in the management of patients with stroke. This is critical because of the variable natural history of stroke ranging from death or severe disability (30-50 percent of patients) to near-complete neurologic recovery of patients treated only with placebo (15 to 30 percent). Tissue not yet abnormal on early diffusion-weighted imaging (DWI) in the presence of reduced blood flow has been postulated to represent a tissue at risk (i.e., the "ischemic penumbra") and therefore a potential therapeutic target. However, there is marked disagreement in the neurology and neuroscience community as to whether or not this MRI mismatch is significant in humans. We have developed techniques to optimize the acquisition of DWI and hemodynamically weighed MRI (HWI), and have demonstrated their use in the setting of clinical acute stroke patient care. We now propose to extend our work to move toward the goal of identifying the presence and extent of acutely salvageable tissue. "Salvageable tissue" is difficult to define, even in the abstract, because one treatment may salvage different tissue than that which is salvageable with another treatment. Therefore, we propose first to improve identification of tissue that is destined to proceed to infarction if no intervention occurs. We will also identify tissue that has markedly variable outcome when untreated, because this tissue - which we hypothesize is the area of DWI/HWI mismatch - may be particularly susceptible to therapy. Our preliminary data suggest that improved statistical modeling can better predict tissue outcome from current input data, and more quantitatively describe the DWI/HWI mismatch. Our data also indicate two novel MRI techniques, diffusion tensor fractional anisotropy and mapping of cerebral blood flow heterogeneity, can further improve the quality of input data. We propose refining and testing our statistical and MRI tools; this will provide data that will determine the presence or absence of MRI markers for therapeutic windows in acute stroke care. Our long-term goal is the development of a tool that can be used to guide clinical practice and to quantitatively evaluate novel therapies.